Senior Technical Project Manager – Machine Learning Science
| Company | Deep Genomics |
|---|---|
| Location | Toronto, ON, Canada |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s |
| Experience Level | Senior |
Requirements
- 5+ years of experience in Technical Project Management, including leading complex, multi-stakeholder projects from planning through execution.
- Proven experience working with ML or Data Science teams; familiarity with the ML lifecycle, including model training, evaluation, and deployment.
- Exceptional organizational and communication skills, with experience engaging stakeholders from engineering, research, and leadership.
- Experience working in agile, cross-functional teams.
- Comfort operating in a fast-paced, research-driven environment where priorities evolve based on new data and discovery.
- Strategic thinking and attention to detail; capable of zooming in and out as needed to support tactical execution and high-level planning.
Responsibilities
- Lead the planning, execution, and delivery of machine learning projects across internal R&D and strategic partnerships.
- Drive quarterly and yearly project planning with ML Scientists and cross-functional stakeholders; define clear goals, outcomes, timelines, and resource needs.
- Establish and maintain robust project management practices tailored to agile, iterative ML workflows.
- Track progress across multiple concurrent initiatives; identify risks and proactively remove blockers to keep teams on track.
- Facilitate strong collaboration across research, engineering, and external partner teams.
- Ensure clear, timely communication of project status, milestones, and risks to internal and external stakeholders.
- Manage and facilitate strategic collaborations with external partners, ensuring alignment, execution, and progress on ML and data-driven initiatives.
Preferred Qualifications
- PMP certification or equivalent.
- Background in biology, genomics, or computational sciences.
- Prior experience working in biotech, life sciences, or scientific R&D settings.
- Familiarity with ML tools (e.g., MLflow, Kubeflow) and cloud platforms (e.g., GCP).